Conceptualization and Non-Relational Implementation of Ontological and Epistemic Vagueness of Information in Digital Humanities

Informatics. 2019;6(2):20 DOI 10.3390/informatics6020020

 

Journal Homepage

Journal Title: Informatics

ISSN: 2227-9709 (Online)

Publisher: MDPI AG

LCC Subject Category: Technology: Technology (General): Industrial engineering. Management engineering: Information technology

Country of publisher: Switzerland

Language of fulltext: English

Full-text formats available: PDF, HTML, XML

 

AUTHORS

Patricia Martin-Rodilla (CiTIUS, University of Santiago de Compostela, Jenaro de la Fuente Domínguez,s/n 15782 Santiago de Compostela, Spain)
Cesar Gonzalez-Perez (Institute of Heritage Sciences (Incipit) Spanish National Research Council (CSIC) Avda. Vigo, s/n. 15705 Santiago de Compostela, Spain)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 6 weeks

 

Abstract | Full Text

Research in the digital humanities often involves vague information, either because our objects of study lack clearly defined boundaries, or because our knowledge about them is incomplete or hypothetical, which is especially true in disciplines about our past (such as history, archaeology, and classical studies). Most techniques used to represent data vagueness emerged from natural sciences, and lack the expressiveness that would be ideal for humanistic contexts. Building on previous work, we present here a conceptual framework based on the ConML modelling language for the expression of information vagueness in digital humanities. In addition, we propose an implementation on non-relational data stores, which are becoming popular within the digital humanities. Having clear implementation guidelines allow us to employ search engines or big data systems (commonly implemented using non-relational approaches) to handle the vague aspects of information. The proposed implementation guidelines have been validated in practice, and show how we can query a vagueness-aware system without a large penalty in analytical and processing power.